The goal of the present work is to demonstrate the potential of Artificial Neural Network(ANN)-driven Genetic Algorithm(GA)methods for energy efficiency and economic performance optimization of energy efficiency measu...The goal of the present work is to demonstrate the potential of Artificial Neural Network(ANN)-driven Genetic Algorithm(GA)methods for energy efficiency and economic performance optimization of energy efficiency measures in a multi-family house building in Greece.The energy efficiency measures include different heating/cooling systems(such as low-temperature and high-temperature heat pumps,natural gas boilers,split units),building envelope components for floor,walls,roof and windows of variable heat transfer coefficients,the installation of solar thermal collectors and PVs.The calculations of the building loads and investment and operating and maintenance costs of the measures are based on the methodology defined in Directive 2010/31/EU,while economic assumptions are based on EN 15459-1 standard.Typically,multi-objective optimization of energy efficiency measures often requires the simulation of very large numbers of cases involving numerous possible combinations,resulting in intense computational load.The results of the study indicate that ANN-driven GA methods can be used as an alternative,valuable tool for reliably predicting the optimal measures which minimize primary energy consumption and life cycle cost of the building with greatly reduced computational requirements.Through GA methods,the computational time needed for obtaining the optimal solutions is reduced by 96.4%-96.8%.展开更多
Coordinating light and nitrogen(N)distribution within a canopy is essential for improving rice yield and resource use efficiency.However,limited research has examined light and N distribution in response to planting d...Coordinating light and nitrogen(N)distribution within a canopy is essential for improving rice yield and resource use efficiency.However,limited research has examined light and N distribution in response to planting density and N rate,and their relationships with grain yield,radiation use efficiency(RUE),and N use efficiency for grain production(NUEg)in rice.A two-year field experiment was conducted with two hybrid varieties under three N levels,0 kg ha^(-1)(N1),90 kg ha^(-1)(N2)and 180 kg ha^(-1)(N3),and two planting densities,22.2 hills m-2(D1)and 33.3 hills m^(-2)(D2).Results showed 3.4%higher yield and 4.4%higher NUEg under N2D2 compared with N3D1.The extinction coefficient for N(K_(N))and light(K_(L))and their ratio(K_(N)/K_(L))at heading stage were significantly influenced by N rate,planting density,and their interaction.K_(N)decreased with the increase of N input or planting density.Compared to N1,K_(N)decreased by 43.5 and 58.8%under N2 and N3,respectively,while K_(N)under D2 decreased by 16.0%compared to D1.Higher K_(L)and K_(N)/K_(L)values occurred under low N rates,with opposite trends under high N rates.Increased planting density led to decreased K_(L)and K_(N)/K_(L)values.N2D2 demonstrated higher K_(L)and K_(N),and thus comparable K_(N)/K_(L),compared to N3D1.Correlation analysis revealed K_(L)negatively correlated with RUE,while K_(N)and K_(N)/K_(L)positively correlated with NUEg.These findings indicate that increasing planting density under reduced N input could maintain rice yield while enhancing resource use efficiency through regulation of canopy light and N distribution.展开更多
Automated essay scoring(AES)systems have gained significant importance in educational settings,offering a scalable,efficient,and objective method for evaluating student essays.However,developing AES systems for Arabic...Automated essay scoring(AES)systems have gained significant importance in educational settings,offering a scalable,efficient,and objective method for evaluating student essays.However,developing AES systems for Arabic poses distinct challenges due to the language’s complex morphology,diglossia,and the scarcity of annotated datasets.This paper presents a hybrid approach to Arabic AES by combining text-based,vector-based,and embeddingbased similarity measures to improve essay scoring accuracy while minimizing the training data required.Using a large Arabic essay dataset categorized into thematic groups,the study conducted four experiments to evaluate the impact of feature selection,data size,and model performance.Experiment 1 established a baseline using a non-machine learning approach,selecting top-N correlated features to predict essay scores.The subsequent experiments employed 5-fold cross-validation.Experiment 2 showed that combining embedding-based,text-based,and vector-based features in a Random Forest(RF)model achieved an R2 of 88.92%and an accuracy of 83.3%within a 0.5-point tolerance.Experiment 3 further refined the feature selection process,demonstrating that 19 correlated features yielded optimal results,improving R2 to 88.95%.In Experiment 4,an optimal data efficiency training approach was introduced,where training data portions increased from 5%to 50%.The study found that using just 10%of the data achieved near-peak performance,with an R2 of 85.49%,emphasizing an effective trade-off between performance and computational costs.These findings highlight the potential of the hybrid approach for developing scalable Arabic AES systems,especially in low-resource environments,addressing linguistic challenges while ensuring efficient data usage.展开更多
In wire arc additive manufacturing(WAAM),a trade-off exists among deposition efficiency,microstructure,and mechanical properties.Addressing this challenge,this work proposes an innovative multi-objective optimization ...In wire arc additive manufacturing(WAAM),a trade-off exists among deposition efficiency,microstructure,and mechanical properties.Addressing this challenge,this work proposes an innovative multi-objective optimization framework tailored for WAAM of AZ31 magnesium alloy components,which integrates deposition efficiency and microstructure as coupled objectives and is resolved through the NSGA-Ⅱ algorithm.The proposed framework employs quadratic regression to correlate process parameters with deposition efficiency through geometric morphology mediation,while addressing uncertainties in WAAM by integrating theoretical insights with data-driven stacked ensemble learning for grain size prediction,establishing the hybrid physics-informed data method for WAAM microstructure prediction.The optimized process achieved a deposition rate of 6257 mm3/min,with effective width and average layer height maintained at 10.1 mm and 4.13 mm,respectively.Microstructural optimization produced a fine,uniform,fully equiaxed grain structure with an average grain size of 38μm.These findings underscore the significant industrial potential of intelligent optimization strategies in WAAM for manufacturing lightweight,high-performance components in aerospace and transportation sectors.展开更多
In this paper,we examine an illegal wireless communication network consisting of an illegal user receiving illegal signals from an illegal station and propose an active reconfigurable intelligent surface(ARIS)-assiste...In this paper,we examine an illegal wireless communication network consisting of an illegal user receiving illegal signals from an illegal station and propose an active reconfigurable intelligent surface(ARIS)-assisted multi-antenna jamming(MAJ)scheme denoted by ARIS-MAJ to interfere with the illegal signal transmission.In order to strike a balance between the jamming performance and the energy consumption,we consider a so-called jamming energy efficiency(JEE)which is defined as the ratio of achievable rate reduced by the jamming system to the corresponding power consumption.We formulate an optimization problem to maximize the JEE for the proposed ARIS-MAJ scheme by jointly optimizing the jammer’s beamforming vector and ARIS’s reflecting coefficients under the constraint that the jamming power received at the illegal user is lower than the illegal user’s detection threshold.To address the non-convex optimization problem,we propose the Dinkelbach-based alternating optimization(AO)algorithm by applying the semidefinite relaxation(SDR)algorithm with Gaussian randomization method.Numerical results validate that the proposed ARIS-MAJ scheme outperforms the passive reconfigurable intelligent surface(PRIS)-assisted multi-antenna jamming(PRIS-MAJ)scheme and the conventional multiantenna jamming scheme without RIS(NRIS-MAJ)in terms of the JEE.展开更多
The optimization of wings typically relies on computationally intensive high-fidelity simulations,which restrict the quick exploration of design spaces.To address this problem,this paper introduces a methodology dedic...The optimization of wings typically relies on computationally intensive high-fidelity simulations,which restrict the quick exploration of design spaces.To address this problem,this paper introduces a methodology dedicated to optimizing box wing configurations using low-fidelity data driven machine learning approach.This technique showcases its practicality through the utilization of a tailored low-fidelity machine learning technique,specifically designed for early-stage wing configuration.By employing surrogate model trained on small dataset derived from low-fidelity simulations,our method aims to predict outputs within an acceptable range.This strategy significantly mitigates computational costs and expedites the design exploration process.The methodology's validation relies on its successful application in optimizing the box wing of PARSIFAL,serving as a benchmark,while the primary focus remains on optimizing the newly designed box wing by Bionica.Applying this method to the Bionica configuration led to a notable 14%improvement in overall aerodynamic effciency.Furthermore,all the optimized results obtained from machine learning model undergo rigorous assessments through the high-fidelity RANS analysis for confirmation.This methodology introduces innovative approach that aims to streamline computational processes,potentially reducing the time and resources required compared to traditional optimization methods.展开更多
Shock waves in the nozzle during supersonic separation under different conditions can disrupt the flow field’s thermodynamic equilibrium.While it contributes to the recovery of pressure energy,it also leads to the di...Shock waves in the nozzle during supersonic separation under different conditions can disrupt the flow field’s thermodynamic equilibrium.While it contributes to the recovery of pressure energy,it also leads to the dissipation of mechanical energy.This study aimed to investigate the effects of changes in back pressure on the shock wave position and its subsequent impact on the refrigeration performance of nozzles.A mathematical model for the supersonic gas in a nozzle was established and evaluated via experiments.The results show that when the back pressure is less than 0.2 MPa,no shock wave is generated in the nozzle,and high refrigeration and liquefaction efficiency can be ensured while effective pressure recovery is achieved.When the back pressure(pb)is increased from 0.3 to 0.6 MPa,the refrigeration efficiency of the nozzle decreases,and the shock wave position(x shock)is advanced from 157 to 118 mm.The maximum Mach number(Ma)that can be reached by the fluid in the nozzle is reduced from 1.97 to 1.27.When the back pressure is increased from 0.2 to 0.6 MPa,the minimum temperature is increased by 55.18 K.When the back pressure is greater than 0.3 MPa,the Mach number upstream of the shock wave is reduced from 1.97 to 1.27,the shock wave intensity is weakened,and the thickness of the boundary layer separation caused by the shock wave is also decreased accordingly.Therefore,to ensure refrigeration efficiency,measures should be taken to control the back pressure within a reasonable range.展开更多
Evaluating the effectivenes s of forest restoration projects is crucial for designing adaptive restoration strategies.However,existing studies have primarily focused on ecological outcomes while overlooking cost input...Evaluating the effectivenes s of forest restoration projects is crucial for designing adaptive restoration strategies.However,existing studies have primarily focused on ecological outcomes while overlooking cost inputs.This gap can lead to increased uncertainties in restoration planning.Here we investigated forest dynamics in China's Upper Yangtze River Basin(UYRB)using kernel Normalized Difference Vegetation Index(kNDVI),Leaf Area Index(LAI),Gross Primary Productivity(GPP),Ku-band Vegetation Optical Depth(Ku-VOD)time series and climate data from1982 to 2020.Subsequently,we employed a residual trend analysis integrating temporal effects to determine the relative contributions of climate change and human activities to forest dynamics before and after the implementation of forest restoration engineering in 1998.Additionally,we developed an Afforestation Efficiency Index(AEI)to quantitatively assess the cost efficiency of afforestation projects.Results indicated that forest in the UYRB showed sustained increases during 1982-2020,with most areas experiencing greater growth after 1998 than before.Temporal effects of climatic factors influenced over 42.7%of the forest,and incorporating time-lag and cumulative effects enhanced climate-based explanations of forest variations by 1.61-24.73%.Human activities emerged as the dominant driver of forest dynamics post 1998,whereas climate variables predominated before this period.The cost-effectiveness of forest restoration projects in the UYRB typically ranges from moderate to high,with higher success predominantly observed in the northeastern and eastern counties,while the central,western,and northwestern counties mainly showed relatively low efficiency.These findings stress the need for assessing forest restoration outcomes from both ecological and cost perspectives,and can offer valuable insights for optimizing the layout of forest restoration initiatives in the UYRB.展开更多
In order to accurately forecast the main engine fuel consumption and reduce the Energy Efficiency Operational Indicator(EEOI)of merchant ships in polar ice areas,the energy transfer relationship between ship-machine-p...In order to accurately forecast the main engine fuel consumption and reduce the Energy Efficiency Operational Indicator(EEOI)of merchant ships in polar ice areas,the energy transfer relationship between ship-machine-propeller is studied by analyzing the complex force situation during ship navigation and building a MATLAB/Simulink simulation platform based on multi-environmental resistance,propeller efficiency,main engine power,fuel consumption,fuel consumption rate and EEOI calculation module.Considering the environmental factors of wind,wave and ice,the route is divided into sections,the calculation of main engine power,main engine fuel consumption and EEOI for each section is completed,and the speed design is optimized based on the simulation model for each section.Under the requirements of the voyage plan,the optimization results show that the energy efficiency operation index of the whole route is reduced by 3.114%and the fuel consumption is reduced by 9.17 t.展开更多
A nonfused ring electron acceptor(NFREA),designated as TT-Ph-C6,has been synthesized with the aim of enhancing the power conversion efficiency(PCE)of organic solar cells(OSCs).By integrating asymmetric phenylalkylamin...A nonfused ring electron acceptor(NFREA),designated as TT-Ph-C6,has been synthesized with the aim of enhancing the power conversion efficiency(PCE)of organic solar cells(OSCs).By integrating asymmetric phenylalkylamino side groups,TT-Ph-C6 demonstrates excellent solubility and its crystal structure exhibits compact packing structures with a three-dimensional molecular stacking network.These structural attributes markedly promote exciton diffusion and charge carrier mobility,particularly advantageous for the fabrication of thick-film devices.TT-Ph-C6-based devices have attained a PCE of 18.01%at a film thickness of 100 nm,and even at a film thickness of 300 nm,the PCE remains at 14.64%,surpassing that of devices based on 2BTh-2F.These remarkable properties position TT-Ph-C6 as a highly promising NFREA material for boosting the efficiency of OSCs.展开更多
Hard carbon is a vital anode material for sodium-ion batteries;however,the nonuniform growth of solid electrolyte interphase(SEI)film substantially diminishes its initial coulombic efficiency(ICE)and cycle life.The ch...Hard carbon is a vital anode material for sodium-ion batteries;however,the nonuniform growth of solid electrolyte interphase(SEI)film substantially diminishes its initial coulombic efficiency(ICE)and cycle life.The chemical and morphological properties of surface highly influence the electrode/electrolyte interfacial reactions.In this study,we have tuned orbital hybridization states forming an interface enriched with sp^(2) hybridized carbon(sp^(2)-C),which decreases the binding energy to solvent molecules and inhibits excessive solvent decomposition during SEI formation.Benefiting from successfully constructed inorganic-rich SEI,the ICE increased to 91%and sodium storage capacity reached 346 mAh/g.Besides,the capacity retention rate was 90.7%after 700 cycles at 1 A/g higher than pristine electrode(83.8%).展开更多
A series of blue and blue‑green Ir(Ⅲ)complexes has been investigated theoretically to explore their electronic structures,photophysical properties,efficiency roll‑off effect,and thermal activation delayed fluorescenc...A series of blue and blue‑green Ir(Ⅲ)complexes has been investigated theoretically to explore their electronic structures,photophysical properties,efficiency roll‑off effect,and thermal activation delayed fluorescence(TADF)properties.All calculations were performed using density functional theory(DFT)and time‑dependent density functional theory(TDDFT).Calculations for electronic structures,frontier molecular orbital characteristics(which determine the efficiency roll‑off effect of the complexes),and photophysical properties were conducted using the Gaussian 09 software package.The calculation of spin‑orbit coupling matrix elements<T|HSOC|S>,which determine the TADF properties of the complexes,was performed using the ORCA software package.The calculation results show that the auxiliary ligand tetraphenylimidodiphosphinate(tpip),a strong electron‑withdrawing group,can mitigate the efficiency roll‑off effect of the complex.Furthermore,TADF is observed in one of the designed complexes,(F_(3)Phppy)_(2)Ir(tpip),where F_(3)Phppy=2‑[4‑(2,4,6‑trifluorophenyl)phenyl]pyridine.展开更多
The effects of micro-ridge-furrow planting(MR)on yield and the efficiency of light,water,and thermal resource use in rapeseed were tested in a three-year field experiment comparing MR to conventional flat planting.MR ...The effects of micro-ridge-furrow planting(MR)on yield and the efficiency of light,water,and thermal resource use in rapeseed were tested in a three-year field experiment comparing MR to conventional flat planting.MR enhanced canopy heterogeneity by altering the leaf angle between plants on ridges and furrows.The heterogeneous canopy environment increased intercepted photosynthetic active radiation,alleviated canopy temperature stress,and optimized canopy humidity,leading to improvements in light-nitrogen matching and net photosynthetic rate.Consequently,dry matter and yield increased by 13.0%and 11.0%,respectively,while radiation,thermal,and precipitation utilization efficiency increased by 12.3%-16.2%.The corresponding improvements in yield and resource use efficiency were attributed to a heterogeneous canopy environment that improved microclimatic conditions.展开更多
To make agricultural systems sustainable in terms of their greenness and efficiency,optimizing the tillage and fertilization practices is essential.To assess the effects of tilling and fertilization practices in wheat...To make agricultural systems sustainable in terms of their greenness and efficiency,optimizing the tillage and fertilization practices is essential.To assess the effects of tilling and fertilization practices in wheat-maize cropping systems,a three-year field experiment was designed to quantify the carbon footprint(CF)and energy efficiency of the cropping systems in the North China Plain.The study parameters included four tillage practices(no tillage(NT),conventional tillage(CT),rotary tillage(RT),and subsoiling rotary tillage(SRT))and two fertilizer regimes(inorganic fertilizer(IF)and hybrid fertilizer with organic and inorganic components(HF)).The results indicated that the most prominent energy inputs and greenhouse gas(GHG)emissions could be ascribed to the use of fertilizers and fuel consumption.Under the same fertilization regime,ranking the tillage patterns with respect to the value of the crop yield,profit,CF,energy use efficiency(EUE)or energy productivity(EP)for either wheat or maize always gave the same sequence of SRT>RT>CT>NT.For the same tillage,the energy consumption associated with HF was higher than IF,but its GHG emissions and CF were lower while the yield and profit were higher.In terms of overall performance,tilling is more beneficial than NT,and reduced tillage practices(RT and SRT)are more beneficial than CT.The fertilization regime with the best overall performance was HF.Combining SRT with HF has significant potential for reducing CF and increasing EUE,thereby improving sustainability.Adopting measures that promote these optimizations can help to overcome the challenges posed by a lack of food security,energy crises and ecological stress.展开更多
Transforming urban spatial structures to promote green and low-carbon development is an effective strategy.Although prior studies have examined the impact of urban polycentricity on carbon emissions and economic devel...Transforming urban spatial structures to promote green and low-carbon development is an effective strategy.Although prior studies have examined the impact of urban polycentricity on carbon emissions and economic development,research on its role in the synergistic relationship between these factors regarding carbon emission efficiency is limited.Furthermore,existing literature often overlooks nonlinear effects and interactions with other urban variables.This paper analyzed data from 295 Chinese cities in 2020,calculating urban population polycentricity,population dispersion indices,and carbon emission efficiency.Utilizing local spatial autocorrelation tools,we reveal interactions among urban population polycentricity,dispersion,carbon emissions,and carbon emission efficiency.We then employ a gradient boosting decision tree model(GBDT)to explore nonlinear and synergistic effects of polycentric urbanization.Key findings include:1)polycentric urbanization in Chinese cities exhibits significant spatial differentiation characteristics.The Polycentricity index is relatively high in economically developed eastern coastal regions with an overall low level,carbon emissions are concentrated in industrialized north-central cities and some Yangtze River Delta hubs,and carbon emission efficiency is the highest in the Yangtze River Delta while relatively low in Northeast China;there are significant spatially heterogeneous interaction characteristics among population polycentricity,population dispersion,carbon emissions,and carbon emission efficiency.2)Urban population polycentricity contributes 9.42%to total carbon emissions and 6.24%to carbon emission efficiency.3)The polycentricity index has a nonlinear impact on carbon emissions and carbon emission efficiency:no significant effect when below 0.50 or above 0.55,increased carbon emissions in 0.50-0.53,and reduced carbon emissions with improved efficiency in 0.53-0.55.4)The polycentricity index has an interaction effect with other variables;specifically,when the polycentricity index is between 0.53 and 0.55,its interaction with urban gross domestic product(GDP),urban population,urban built-up area,green coverage rate in built-up areas,urban technological expenditure,and the proportion of the output value of the secondary industry will reduce carbon emissions and improve carbon emission efficiency.These findings enhance the understanding of urban spatial structures and carbon emissions,providing valuable insights for policymakers in developing green and low-carbon strategies.展开更多
We incorporate a non-Markovian feedback mechanism into the simulated bifurcation method for dynamical solvers addressing combinatorial optimization problems.By reinjecting a portion of dissipated kinetic energy into e...We incorporate a non-Markovian feedback mechanism into the simulated bifurcation method for dynamical solvers addressing combinatorial optimization problems.By reinjecting a portion of dissipated kinetic energy into each spin in a history-dependent and trajectory-informed manner,the method effectively suppresses early freezing induced by inelastic boundaries and enhances the system's ability to explore complex energy landscapes.Numerical results on the maximum cut(MAX-CUT)instances of fully connected Sherrington–Kirkpatrick(SK)spin glass models,including the 2000-spin K_(2000)benchmark,demonstrate that the non-Markovian algorithm significantly improves both solution quality and convergence speed.Tests on randomly generated SK instances with 100 to 1000 spins further indicate favorable scalability and substantial gains in computational efficiency.Moreover,the proposed scheme is well suited for massively parallel hardware implementations,such as field-programmable gate arrays,providing a practical and scalable approach for solving large-scale combinatorial optimization problems.展开更多
A high-sensitivity,low-noise single photon avalanche diode(SPAD)detector was presented based on a 180 nm BCD process.The proposed device utilizes a p-implant layer/high-voltage n-well(HVNW)junction to form a deep aval...A high-sensitivity,low-noise single photon avalanche diode(SPAD)detector was presented based on a 180 nm BCD process.The proposed device utilizes a p-implant layer/high-voltage n-well(HVNW)junction to form a deep avalanche multiplication region for near-infrared(NIR)sensitivity enhancement.By optimizing the device size and electric field of the guard ring,the fill factor(FF)is significantly improved,further increasing photon detection efficiency(PDE).To solve the dark noise caused by the increasing active diameter,a field polysilicon gate structure connected to the p+anode was investigated,effectively suppressing dark count noise by 76.6%.It is experimentally shown that when the active diameter increases from 5 to 10μm,the FF is significantly improved from 20.7%to 39.1%,and thus the peak PDE also rises from 13.3%to 25.8%.At an excess bias voltage of 5 V,a NIR photon detection probability(PDP)of 6.8%at 905 nm,a dark count rate(DCR)of 2.12 cps/μm^(2),an afterpulsing probability(AP)of 1.2%,and a timing jitter of 216 ps are achieved,demonstrating excellent single photon detection performance.展开更多
AI’s(artificial intelligence)groundbreaking impact on energy optimization and efficiency across various fields is growing,minimizing costs,increasing environmental sustainability,and improving energy resource managem...AI’s(artificial intelligence)groundbreaking impact on energy optimization and efficiency across various fields is growing,minimizing costs,increasing environmental sustainability,and improving energy resource management.As the global energy demand is predicted to rise,traditional energy management methods are proved to be inefficient,calling for new,innovative AI-driven solutions.This research unfolds the revolutionary impact of AI in energy optimization,focusing on its modern approaches,most significantly,predictive maintenance and analytics.A notable achievement is reflected by Stem Inc.,whose AI-powered energy storage system reduced its electricity costs by 60%,through predictive analytics of demand-based battery charging and discharging.Additionally,the study also investigates the logic behind AI’s energy optimization methods and AI’s role in crucial sectors like oil extraction,solar energy maintenance,and smart buildings,showcasing its flexibility across various fields.Finally,the study also uncovers a groundbreaking solution to improve AI’s role in energy optimization.Ultimately,this paper highlights the significance of AI in energy optimization and efficiency in the 21st century,the current methods used,and its projected growth and potential in the future.展开更多
The rapid expansion of the Internet of Things(IoT)has led to the widespread adoption of sensor networks,with Long-Range Wide-Area Networks(LoRaWANs)emerging as a key technology due to their ability to support long-ran...The rapid expansion of the Internet of Things(IoT)has led to the widespread adoption of sensor networks,with Long-Range Wide-Area Networks(LoRaWANs)emerging as a key technology due to their ability to support long-range communication while minimizing power consumption.However,optimizing network performance and energy efficiency in dynamic,large-scale IoT environments remains a significant challenge.Traditional methods,such as the Adaptive Data Rate(ADR)algorithm,often fail to adapt effectively to rapidly changing network conditions and environmental factors.This study introduces a hybrid approach that leverages Deep Learning(DL)techniques,namely Long Short-Term Memory(LSTM)networks,and Machine Learning(ML)techniques,namely Artificial Neural Networks(ANNs),to optimize key network parameters such as Signal-to-Noise Ratio(SNR)and Received Signal Strength Indicator(RSSI).LSTM-ANN model trained on the“LoRaWAN Path Loss Dataset including Environmental Variables”from Medellín,Colombia,and the model demonstrated exceptional predictive accuracy,achieving an R2 score of 0.999,Mean Squared Error(MSE)of 0.041,Root Mean Squared Error(RMSE)of 0.203,and Mean Absolute Error(MAE)of 0.167,significantly outperforming traditional regression-based approaches.These findings highlight the potential of combining advanced ML and DL techniques to address the limitations of traditional optimization strategies in LoRaWAN.By providing a scalable and adaptive solution for large-scale IoT deployments,this work lays the foundation for real-world implementation,emphasizing the need for continuous learning frameworks to further enhance energy efficiency and network resilience in dynamic environments.展开更多
Water power is one of the key renewable energy resources,whose efficiency is often hampered due to inefficient water flow management,turbine performance,and environmental variations.Most existing optimization techniqu...Water power is one of the key renewable energy resources,whose efficiency is often hampered due to inefficient water flow management,turbine performance,and environmental variations.Most existing optimization techniques lack the real-time adaptability to sufficiently allocate resources in terms of location and time.Hence,a novel Scalable Tas-manian Devil Optimization(STDO)algorithm is introduced to optimize hydropower generation for maximum power efficiency.Using the STDO to model important system characteristics including water flow,turbine changes,and energy conversion efficiency is part of the process.In the final analysis,optimizing these settings in would help reduce inefficiencies and maximize power generation output.Following that,simulations based on actual hydroelectric data are used to analyze the algorithm's effectiveness.The simulation results provide evidence that the STDO algorithm can enhance hydropower plant efficiency tremendously translating to considerable energy output augmentation compared to conven-tional optimization methods.STDO achieves the reliability(92.5),resiliency(74.3),and reduced vulnerability(9.3).To guarantee increased efficiency towards ecologically friendly power generation,the STDO algorithm may thus offer efficient resource optimization for hydropower.A clear route is made available for expanding the efficiency of current hydropower facilities while tackling the long-term objectives of reducing the environmental impact and increasing the energy output of energy produced from renewable sources.展开更多
文摘The goal of the present work is to demonstrate the potential of Artificial Neural Network(ANN)-driven Genetic Algorithm(GA)methods for energy efficiency and economic performance optimization of energy efficiency measures in a multi-family house building in Greece.The energy efficiency measures include different heating/cooling systems(such as low-temperature and high-temperature heat pumps,natural gas boilers,split units),building envelope components for floor,walls,roof and windows of variable heat transfer coefficients,the installation of solar thermal collectors and PVs.The calculations of the building loads and investment and operating and maintenance costs of the measures are based on the methodology defined in Directive 2010/31/EU,while economic assumptions are based on EN 15459-1 standard.Typically,multi-objective optimization of energy efficiency measures often requires the simulation of very large numbers of cases involving numerous possible combinations,resulting in intense computational load.The results of the study indicate that ANN-driven GA methods can be used as an alternative,valuable tool for reliably predicting the optimal measures which minimize primary energy consumption and life cycle cost of the building with greatly reduced computational requirements.Through GA methods,the computational time needed for obtaining the optimal solutions is reduced by 96.4%-96.8%.
基金supported by the Hubei Provincial Science and Technology Project,China(2025CSA039)the National Natural Science Foundation of China(32001467)。
文摘Coordinating light and nitrogen(N)distribution within a canopy is essential for improving rice yield and resource use efficiency.However,limited research has examined light and N distribution in response to planting density and N rate,and their relationships with grain yield,radiation use efficiency(RUE),and N use efficiency for grain production(NUEg)in rice.A two-year field experiment was conducted with two hybrid varieties under three N levels,0 kg ha^(-1)(N1),90 kg ha^(-1)(N2)and 180 kg ha^(-1)(N3),and two planting densities,22.2 hills m-2(D1)and 33.3 hills m^(-2)(D2).Results showed 3.4%higher yield and 4.4%higher NUEg under N2D2 compared with N3D1.The extinction coefficient for N(K_(N))and light(K_(L))and their ratio(K_(N)/K_(L))at heading stage were significantly influenced by N rate,planting density,and their interaction.K_(N)decreased with the increase of N input or planting density.Compared to N1,K_(N)decreased by 43.5 and 58.8%under N2 and N3,respectively,while K_(N)under D2 decreased by 16.0%compared to D1.Higher K_(L)and K_(N)/K_(L)values occurred under low N rates,with opposite trends under high N rates.Increased planting density led to decreased K_(L)and K_(N)/K_(L)values.N2D2 demonstrated higher K_(L)and K_(N),and thus comparable K_(N)/K_(L),compared to N3D1.Correlation analysis revealed K_(L)negatively correlated with RUE,while K_(N)and K_(N)/K_(L)positively correlated with NUEg.These findings indicate that increasing planting density under reduced N input could maintain rice yield while enhancing resource use efficiency through regulation of canopy light and N distribution.
基金funded by Deanship of Graduate studies and Scientific Research at Jouf University under grant No.(DGSSR-2024-02-01264).
文摘Automated essay scoring(AES)systems have gained significant importance in educational settings,offering a scalable,efficient,and objective method for evaluating student essays.However,developing AES systems for Arabic poses distinct challenges due to the language’s complex morphology,diglossia,and the scarcity of annotated datasets.This paper presents a hybrid approach to Arabic AES by combining text-based,vector-based,and embeddingbased similarity measures to improve essay scoring accuracy while minimizing the training data required.Using a large Arabic essay dataset categorized into thematic groups,the study conducted four experiments to evaluate the impact of feature selection,data size,and model performance.Experiment 1 established a baseline using a non-machine learning approach,selecting top-N correlated features to predict essay scores.The subsequent experiments employed 5-fold cross-validation.Experiment 2 showed that combining embedding-based,text-based,and vector-based features in a Random Forest(RF)model achieved an R2 of 88.92%and an accuracy of 83.3%within a 0.5-point tolerance.Experiment 3 further refined the feature selection process,demonstrating that 19 correlated features yielded optimal results,improving R2 to 88.95%.In Experiment 4,an optimal data efficiency training approach was introduced,where training data portions increased from 5%to 50%.The study found that using just 10%of the data achieved near-peak performance,with an R2 of 85.49%,emphasizing an effective trade-off between performance and computational costs.These findings highlight the potential of the hybrid approach for developing scalable Arabic AES systems,especially in low-resource environments,addressing linguistic challenges while ensuring efficient data usage.
基金supported by the National Natural Science Foundation of China(Nos.52475317 and 52305331).
文摘In wire arc additive manufacturing(WAAM),a trade-off exists among deposition efficiency,microstructure,and mechanical properties.Addressing this challenge,this work proposes an innovative multi-objective optimization framework tailored for WAAM of AZ31 magnesium alloy components,which integrates deposition efficiency and microstructure as coupled objectives and is resolved through the NSGA-Ⅱ algorithm.The proposed framework employs quadratic regression to correlate process parameters with deposition efficiency through geometric morphology mediation,while addressing uncertainties in WAAM by integrating theoretical insights with data-driven stacked ensemble learning for grain size prediction,establishing the hybrid physics-informed data method for WAAM microstructure prediction.The optimized process achieved a deposition rate of 6257 mm3/min,with effective width and average layer height maintained at 10.1 mm and 4.13 mm,respectively.Microstructural optimization produced a fine,uniform,fully equiaxed grain structure with an average grain size of 38μm.These findings underscore the significant industrial potential of intelligent optimization strategies in WAAM for manufacturing lightweight,high-performance components in aerospace and transportation sectors.
基金supported in part by the National Natural Science Foundation of China under Grant 62071253,Grant 62371252 and Grant 62271268in part by the Jiangsu Provincial Key Research and Development Program under Grant BE2022800in part by the Jiangsu Provincial 333 Talent Project.
文摘In this paper,we examine an illegal wireless communication network consisting of an illegal user receiving illegal signals from an illegal station and propose an active reconfigurable intelligent surface(ARIS)-assisted multi-antenna jamming(MAJ)scheme denoted by ARIS-MAJ to interfere with the illegal signal transmission.In order to strike a balance between the jamming performance and the energy consumption,we consider a so-called jamming energy efficiency(JEE)which is defined as the ratio of achievable rate reduced by the jamming system to the corresponding power consumption.We formulate an optimization problem to maximize the JEE for the proposed ARIS-MAJ scheme by jointly optimizing the jammer’s beamforming vector and ARIS’s reflecting coefficients under the constraint that the jamming power received at the illegal user is lower than the illegal user’s detection threshold.To address the non-convex optimization problem,we propose the Dinkelbach-based alternating optimization(AO)algorithm by applying the semidefinite relaxation(SDR)algorithm with Gaussian randomization method.Numerical results validate that the proposed ARIS-MAJ scheme outperforms the passive reconfigurable intelligent surface(PRIS)-assisted multi-antenna jamming(PRIS-MAJ)scheme and the conventional multiantenna jamming scheme without RIS(NRIS-MAJ)in terms of the JEE.
基金The funding for this publication was provided by Johannes Kepler University(JKU),Linz.Special thanks to Prof.Zongmin DENG from Beihang University for his invaluable guidance,insightful feedback,and constructive criticism,which greatly enhanced the quality of this manuscript.We extend our heartfelt gratitude to the PARSIFAL team for providing the supporting materials,which inspired this study.
文摘The optimization of wings typically relies on computationally intensive high-fidelity simulations,which restrict the quick exploration of design spaces.To address this problem,this paper introduces a methodology dedicated to optimizing box wing configurations using low-fidelity data driven machine learning approach.This technique showcases its practicality through the utilization of a tailored low-fidelity machine learning technique,specifically designed for early-stage wing configuration.By employing surrogate model trained on small dataset derived from low-fidelity simulations,our method aims to predict outputs within an acceptable range.This strategy significantly mitigates computational costs and expedites the design exploration process.The methodology's validation relies on its successful application in optimizing the box wing of PARSIFAL,serving as a benchmark,while the primary focus remains on optimizing the newly designed box wing by Bionica.Applying this method to the Bionica configuration led to a notable 14%improvement in overall aerodynamic effciency.Furthermore,all the optimized results obtained from machine learning model undergo rigorous assessments through the high-fidelity RANS analysis for confirmation.This methodology introduces innovative approach that aims to streamline computational processes,potentially reducing the time and resources required compared to traditional optimization methods.
基金supported by the National Science and Technology Major Project of China(2025ZD1406703)the Open Fund of Key Laboratory of Oil&Gas Equipment,Ministry of Education(Southwest Petroleum University)(Grant No.OGE20230206).
文摘Shock waves in the nozzle during supersonic separation under different conditions can disrupt the flow field’s thermodynamic equilibrium.While it contributes to the recovery of pressure energy,it also leads to the dissipation of mechanical energy.This study aimed to investigate the effects of changes in back pressure on the shock wave position and its subsequent impact on the refrigeration performance of nozzles.A mathematical model for the supersonic gas in a nozzle was established and evaluated via experiments.The results show that when the back pressure is less than 0.2 MPa,no shock wave is generated in the nozzle,and high refrigeration and liquefaction efficiency can be ensured while effective pressure recovery is achieved.When the back pressure(pb)is increased from 0.3 to 0.6 MPa,the refrigeration efficiency of the nozzle decreases,and the shock wave position(x shock)is advanced from 157 to 118 mm.The maximum Mach number(Ma)that can be reached by the fluid in the nozzle is reduced from 1.97 to 1.27.When the back pressure is increased from 0.2 to 0.6 MPa,the minimum temperature is increased by 55.18 K.When the back pressure is greater than 0.3 MPa,the Mach number upstream of the shock wave is reduced from 1.97 to 1.27,the shock wave intensity is weakened,and the thickness of the boundary layer separation caused by the shock wave is also decreased accordingly.Therefore,to ensure refrigeration efficiency,measures should be taken to control the back pressure within a reasonable range.
基金supported by the National Natural Science Foundation of China(42071238)the Jiuzhaigou Post-Disaster Restoration and Reconstruction Program(5132202020000046)+1 种基金the Second Tibetan Plateau Scientific Expedition and Research Program(STEP)the Ministry of Science and Technology of the People's Republic of China(2019QZKK0402)。
文摘Evaluating the effectivenes s of forest restoration projects is crucial for designing adaptive restoration strategies.However,existing studies have primarily focused on ecological outcomes while overlooking cost inputs.This gap can lead to increased uncertainties in restoration planning.Here we investigated forest dynamics in China's Upper Yangtze River Basin(UYRB)using kernel Normalized Difference Vegetation Index(kNDVI),Leaf Area Index(LAI),Gross Primary Productivity(GPP),Ku-band Vegetation Optical Depth(Ku-VOD)time series and climate data from1982 to 2020.Subsequently,we employed a residual trend analysis integrating temporal effects to determine the relative contributions of climate change and human activities to forest dynamics before and after the implementation of forest restoration engineering in 1998.Additionally,we developed an Afforestation Efficiency Index(AEI)to quantitatively assess the cost efficiency of afforestation projects.Results indicated that forest in the UYRB showed sustained increases during 1982-2020,with most areas experiencing greater growth after 1998 than before.Temporal effects of climatic factors influenced over 42.7%of the forest,and incorporating time-lag and cumulative effects enhanced climate-based explanations of forest variations by 1.61-24.73%.Human activities emerged as the dominant driver of forest dynamics post 1998,whereas climate variables predominated before this period.The cost-effectiveness of forest restoration projects in the UYRB typically ranges from moderate to high,with higher success predominantly observed in the northeastern and eastern counties,while the central,western,and northwestern counties mainly showed relatively low efficiency.These findings stress the need for assessing forest restoration outcomes from both ecological and cost perspectives,and can offer valuable insights for optimizing the layout of forest restoration initiatives in the UYRB.
文摘In order to accurately forecast the main engine fuel consumption and reduce the Energy Efficiency Operational Indicator(EEOI)of merchant ships in polar ice areas,the energy transfer relationship between ship-machine-propeller is studied by analyzing the complex force situation during ship navigation and building a MATLAB/Simulink simulation platform based on multi-environmental resistance,propeller efficiency,main engine power,fuel consumption,fuel consumption rate and EEOI calculation module.Considering the environmental factors of wind,wave and ice,the route is divided into sections,the calculation of main engine power,main engine fuel consumption and EEOI for each section is completed,and the speed design is optimized based on the simulation model for each section.Under the requirements of the voyage plan,the optimization results show that the energy efficiency operation index of the whole route is reduced by 3.114%and the fuel consumption is reduced by 9.17 t.
基金Financial support from the National Natural Science Foundation of China(22375024,21975031,21734009,51933001,22109080,and 52173174)the Natural Science Foundation of Shandong Province(No.ZR2022YQ45)+2 种基金the Taishan Scholars Program(Nos.tstp20221121 and tsqnz20221134)The Beijing Natural Science Foundation(No.2244073)supported by State Key Laboratory of Bio-Fibers and Eco-Textiles(Qingdao University)(RZ2200002821)is acknowledged.
文摘A nonfused ring electron acceptor(NFREA),designated as TT-Ph-C6,has been synthesized with the aim of enhancing the power conversion efficiency(PCE)of organic solar cells(OSCs).By integrating asymmetric phenylalkylamino side groups,TT-Ph-C6 demonstrates excellent solubility and its crystal structure exhibits compact packing structures with a three-dimensional molecular stacking network.These structural attributes markedly promote exciton diffusion and charge carrier mobility,particularly advantageous for the fabrication of thick-film devices.TT-Ph-C6-based devices have attained a PCE of 18.01%at a film thickness of 100 nm,and even at a film thickness of 300 nm,the PCE remains at 14.64%,surpassing that of devices based on 2BTh-2F.These remarkable properties position TT-Ph-C6 as a highly promising NFREA material for boosting the efficiency of OSCs.
基金support from the Heilongjiang Province"Double First Class"Discipline Collaborative Innovation Project(No.LJGXCG2023-061).
文摘Hard carbon is a vital anode material for sodium-ion batteries;however,the nonuniform growth of solid electrolyte interphase(SEI)film substantially diminishes its initial coulombic efficiency(ICE)and cycle life.The chemical and morphological properties of surface highly influence the electrode/electrolyte interfacial reactions.In this study,we have tuned orbital hybridization states forming an interface enriched with sp^(2) hybridized carbon(sp^(2)-C),which decreases the binding energy to solvent molecules and inhibits excessive solvent decomposition during SEI formation.Benefiting from successfully constructed inorganic-rich SEI,the ICE increased to 91%and sodium storage capacity reached 346 mAh/g.Besides,the capacity retention rate was 90.7%after 700 cycles at 1 A/g higher than pristine electrode(83.8%).
文摘A series of blue and blue‑green Ir(Ⅲ)complexes has been investigated theoretically to explore their electronic structures,photophysical properties,efficiency roll‑off effect,and thermal activation delayed fluorescence(TADF)properties.All calculations were performed using density functional theory(DFT)and time‑dependent density functional theory(TDDFT).Calculations for electronic structures,frontier molecular orbital characteristics(which determine the efficiency roll‑off effect of the complexes),and photophysical properties were conducted using the Gaussian 09 software package.The calculation of spin‑orbit coupling matrix elements<T|HSOC|S>,which determine the TADF properties of the complexes,was performed using the ORCA software package.The calculation results show that the auxiliary ligand tetraphenylimidodiphosphinate(tpip),a strong electron‑withdrawing group,can mitigate the efficiency roll‑off effect of the complex.Furthermore,TADF is observed in one of the designed complexes,(F_(3)Phppy)_(2)Ir(tpip),where F_(3)Phppy=2‑[4‑(2,4,6‑trifluorophenyl)phenyl]pyridine.
基金supported by the National Key Research and Development Program of China (2021YFD1901200)the Key Research and Development Program of Hubei Province of China (2023BBB028)+1 种基金the Earmarked Fund of Hubei province of Chinathe Fundamental Research Funds for the Central Universities (2662024ZKQD005)
文摘The effects of micro-ridge-furrow planting(MR)on yield and the efficiency of light,water,and thermal resource use in rapeseed were tested in a three-year field experiment comparing MR to conventional flat planting.MR enhanced canopy heterogeneity by altering the leaf angle between plants on ridges and furrows.The heterogeneous canopy environment increased intercepted photosynthetic active radiation,alleviated canopy temperature stress,and optimized canopy humidity,leading to improvements in light-nitrogen matching and net photosynthetic rate.Consequently,dry matter and yield increased by 13.0%and 11.0%,respectively,while radiation,thermal,and precipitation utilization efficiency increased by 12.3%-16.2%.The corresponding improvements in yield and resource use efficiency were attributed to a heterogeneous canopy environment that improved microclimatic conditions.
基金supported by research grants from the Natural Science Foundation of Shandong Province,China(ZR2020MC092)the Key Research and Development Project of Shandong Province,China(2019TSCYCX-33)the Key Research and Development Project of Shandong Province,China(LJNY202025).
文摘To make agricultural systems sustainable in terms of their greenness and efficiency,optimizing the tillage and fertilization practices is essential.To assess the effects of tilling and fertilization practices in wheat-maize cropping systems,a three-year field experiment was designed to quantify the carbon footprint(CF)and energy efficiency of the cropping systems in the North China Plain.The study parameters included four tillage practices(no tillage(NT),conventional tillage(CT),rotary tillage(RT),and subsoiling rotary tillage(SRT))and two fertilizer regimes(inorganic fertilizer(IF)and hybrid fertilizer with organic and inorganic components(HF)).The results indicated that the most prominent energy inputs and greenhouse gas(GHG)emissions could be ascribed to the use of fertilizers and fuel consumption.Under the same fertilization regime,ranking the tillage patterns with respect to the value of the crop yield,profit,CF,energy use efficiency(EUE)or energy productivity(EP)for either wheat or maize always gave the same sequence of SRT>RT>CT>NT.For the same tillage,the energy consumption associated with HF was higher than IF,but its GHG emissions and CF were lower while the yield and profit were higher.In terms of overall performance,tilling is more beneficial than NT,and reduced tillage practices(RT and SRT)are more beneficial than CT.The fertilization regime with the best overall performance was HF.Combining SRT with HF has significant potential for reducing CF and increasing EUE,thereby improving sustainability.Adopting measures that promote these optimizations can help to overcome the challenges posed by a lack of food security,energy crises and ecological stress.
基金Under the auspices of National Natural Science Foundation of China(No.42571300)。
文摘Transforming urban spatial structures to promote green and low-carbon development is an effective strategy.Although prior studies have examined the impact of urban polycentricity on carbon emissions and economic development,research on its role in the synergistic relationship between these factors regarding carbon emission efficiency is limited.Furthermore,existing literature often overlooks nonlinear effects and interactions with other urban variables.This paper analyzed data from 295 Chinese cities in 2020,calculating urban population polycentricity,population dispersion indices,and carbon emission efficiency.Utilizing local spatial autocorrelation tools,we reveal interactions among urban population polycentricity,dispersion,carbon emissions,and carbon emission efficiency.We then employ a gradient boosting decision tree model(GBDT)to explore nonlinear and synergistic effects of polycentric urbanization.Key findings include:1)polycentric urbanization in Chinese cities exhibits significant spatial differentiation characteristics.The Polycentricity index is relatively high in economically developed eastern coastal regions with an overall low level,carbon emissions are concentrated in industrialized north-central cities and some Yangtze River Delta hubs,and carbon emission efficiency is the highest in the Yangtze River Delta while relatively low in Northeast China;there are significant spatially heterogeneous interaction characteristics among population polycentricity,population dispersion,carbon emissions,and carbon emission efficiency.2)Urban population polycentricity contributes 9.42%to total carbon emissions and 6.24%to carbon emission efficiency.3)The polycentricity index has a nonlinear impact on carbon emissions and carbon emission efficiency:no significant effect when below 0.50 or above 0.55,increased carbon emissions in 0.50-0.53,and reduced carbon emissions with improved efficiency in 0.53-0.55.4)The polycentricity index has an interaction effect with other variables;specifically,when the polycentricity index is between 0.53 and 0.55,its interaction with urban gross domestic product(GDP),urban population,urban built-up area,green coverage rate in built-up areas,urban technological expenditure,and the proportion of the output value of the secondary industry will reduce carbon emissions and improve carbon emission efficiency.These findings enhance the understanding of urban spatial structures and carbon emissions,providing valuable insights for policymakers in developing green and low-carbon strategies.
基金supported by the National Key Research and Development Program of China(Grant No.2024YFA1408500)the National Natural Science Foundation of China(Grant Nos.12174028 and 12574115)the Open Fund of the State Key Laboratory of Spintronics Devices and Technologies(Grant No.SPL-2408)。
文摘We incorporate a non-Markovian feedback mechanism into the simulated bifurcation method for dynamical solvers addressing combinatorial optimization problems.By reinjecting a portion of dissipated kinetic energy into each spin in a history-dependent and trajectory-informed manner,the method effectively suppresses early freezing induced by inelastic boundaries and enhances the system's ability to explore complex energy landscapes.Numerical results on the maximum cut(MAX-CUT)instances of fully connected Sherrington–Kirkpatrick(SK)spin glass models,including the 2000-spin K_(2000)benchmark,demonstrate that the non-Markovian algorithm significantly improves both solution quality and convergence speed.Tests on randomly generated SK instances with 100 to 1000 spins further indicate favorable scalability and substantial gains in computational efficiency.Moreover,the proposed scheme is well suited for massively parallel hardware implementations,such as field-programmable gate arrays,providing a practical and scalable approach for solving large-scale combinatorial optimization problems.
基金supported by the National Natural Science Foundation of China under Grant 62171233the Natural Science Foundation of China,Jiangsu Province under Grant BK20241891the Jiangsu Province Graduate Research and Practice Innovation Plan under Grants SJCX24_0313 and KYCX24_1169。
文摘A high-sensitivity,low-noise single photon avalanche diode(SPAD)detector was presented based on a 180 nm BCD process.The proposed device utilizes a p-implant layer/high-voltage n-well(HVNW)junction to form a deep avalanche multiplication region for near-infrared(NIR)sensitivity enhancement.By optimizing the device size and electric field of the guard ring,the fill factor(FF)is significantly improved,further increasing photon detection efficiency(PDE).To solve the dark noise caused by the increasing active diameter,a field polysilicon gate structure connected to the p+anode was investigated,effectively suppressing dark count noise by 76.6%.It is experimentally shown that when the active diameter increases from 5 to 10μm,the FF is significantly improved from 20.7%to 39.1%,and thus the peak PDE also rises from 13.3%to 25.8%.At an excess bias voltage of 5 V,a NIR photon detection probability(PDP)of 6.8%at 905 nm,a dark count rate(DCR)of 2.12 cps/μm^(2),an afterpulsing probability(AP)of 1.2%,and a timing jitter of 216 ps are achieved,demonstrating excellent single photon detection performance.
文摘AI’s(artificial intelligence)groundbreaking impact on energy optimization and efficiency across various fields is growing,minimizing costs,increasing environmental sustainability,and improving energy resource management.As the global energy demand is predicted to rise,traditional energy management methods are proved to be inefficient,calling for new,innovative AI-driven solutions.This research unfolds the revolutionary impact of AI in energy optimization,focusing on its modern approaches,most significantly,predictive maintenance and analytics.A notable achievement is reflected by Stem Inc.,whose AI-powered energy storage system reduced its electricity costs by 60%,through predictive analytics of demand-based battery charging and discharging.Additionally,the study also investigates the logic behind AI’s energy optimization methods and AI’s role in crucial sectors like oil extraction,solar energy maintenance,and smart buildings,showcasing its flexibility across various fields.Finally,the study also uncovers a groundbreaking solution to improve AI’s role in energy optimization.Ultimately,this paper highlights the significance of AI in energy optimization and efficiency in the 21st century,the current methods used,and its projected growth and potential in the future.
基金funded by King Saud University Researchers Supporting Project Number(RSPD2025R1007),King Saud University,Riyadh,Saudi Arabia.
文摘The rapid expansion of the Internet of Things(IoT)has led to the widespread adoption of sensor networks,with Long-Range Wide-Area Networks(LoRaWANs)emerging as a key technology due to their ability to support long-range communication while minimizing power consumption.However,optimizing network performance and energy efficiency in dynamic,large-scale IoT environments remains a significant challenge.Traditional methods,such as the Adaptive Data Rate(ADR)algorithm,often fail to adapt effectively to rapidly changing network conditions and environmental factors.This study introduces a hybrid approach that leverages Deep Learning(DL)techniques,namely Long Short-Term Memory(LSTM)networks,and Machine Learning(ML)techniques,namely Artificial Neural Networks(ANNs),to optimize key network parameters such as Signal-to-Noise Ratio(SNR)and Received Signal Strength Indicator(RSSI).LSTM-ANN model trained on the“LoRaWAN Path Loss Dataset including Environmental Variables”from Medellín,Colombia,and the model demonstrated exceptional predictive accuracy,achieving an R2 score of 0.999,Mean Squared Error(MSE)of 0.041,Root Mean Squared Error(RMSE)of 0.203,and Mean Absolute Error(MAE)of 0.167,significantly outperforming traditional regression-based approaches.These findings highlight the potential of combining advanced ML and DL techniques to address the limitations of traditional optimization strategies in LoRaWAN.By providing a scalable and adaptive solution for large-scale IoT deployments,this work lays the foundation for real-world implementation,emphasizing the need for continuous learning frameworks to further enhance energy efficiency and network resilience in dynamic environments.
文摘Water power is one of the key renewable energy resources,whose efficiency is often hampered due to inefficient water flow management,turbine performance,and environmental variations.Most existing optimization techniques lack the real-time adaptability to sufficiently allocate resources in terms of location and time.Hence,a novel Scalable Tas-manian Devil Optimization(STDO)algorithm is introduced to optimize hydropower generation for maximum power efficiency.Using the STDO to model important system characteristics including water flow,turbine changes,and energy conversion efficiency is part of the process.In the final analysis,optimizing these settings in would help reduce inefficiencies and maximize power generation output.Following that,simulations based on actual hydroelectric data are used to analyze the algorithm's effectiveness.The simulation results provide evidence that the STDO algorithm can enhance hydropower plant efficiency tremendously translating to considerable energy output augmentation compared to conven-tional optimization methods.STDO achieves the reliability(92.5),resiliency(74.3),and reduced vulnerability(9.3).To guarantee increased efficiency towards ecologically friendly power generation,the STDO algorithm may thus offer efficient resource optimization for hydropower.A clear route is made available for expanding the efficiency of current hydropower facilities while tackling the long-term objectives of reducing the environmental impact and increasing the energy output of energy produced from renewable sources.
